Gasvem: a new machine learning methodology for multi‐snp analysis of gwas data based on genetic algorithms and support vector machines

dc.contributor.author	Díez Díaz, Fidel
dc.contributor.author	Sánchez Lasheras, Fernando
dc.contributor.author	Moreno, V.
dc.contributor.author	Moratalla Navarro, F.
dc.contributor.author	Molina de la Torre, Antonio José
dc.contributor.author	Martín Sánchez, Vicente
dc.date.accessioned	2021-07-26T07:50:27Z
dc.date.available	2021-07-26T07:50:27Z
dc.date.issued	2021
dc.identifier.citation	Mathematics, 9(6) (2021); doi:10.3390/math9060654
dc.identifier.issn	2227-7390
dc.identifier.uri	http://hdl.handle.net/10651/60003
dc.description.sponsorship	Agency for Management of University and Research Grants (AGAUR) of the Catalan Government [2017SGR723]; Instituto de Salud Carlos III, co-funded by FEDER funds -a way to build Europe- grants; Spanish Association Against Cancer (AECC), Scientific Foundation grant GCTRA18022MORE.
dc.language.iso	eng
dc.relation.ispartof	Mathematics
dc.rights	© 2021 Los autores
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103569792&doi=10.3390%2fmath9060654&partnerID=40&md5=14e23aecd148559a16c0b5c50a520556
dc.title	Gasvem: a new machine learning methodology for multi‐snp analysis of gwas data based on genetic algorithms and support vector machines
dc.type	journal article
dc.identifier.doi	10.3390/math9060654
dc.relation.publisherversion	http://dx.doi.org/10.3390/math9060654
dc.rights.accessRights	open access
dc.type.hasVersion	VoR

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